Note: If you are concatenating along a new axis consider using stack.
E.g.

tf.concat([tf.expand_dims(t, axis) for t in tensors], axis)

can be rewritten as

tf.stack(tensors, axis=axis)

Args:

values: A list of Tensor objects or a single Tensor.

axis: 0-D int32Tensor. Dimension along which to concatenate. Must be
in the range [-rank(values), rank(values)). As in Python, indexing
for axis is 0-based. Positive axis in the rage of
[0, rank(values)) refers to axis-th dimension. And negative axis
refers to axis + rank(values)-th dimension.